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- Tasks: Build and maintain geospatial data pipelines and APIs using cutting-edge technologies.
- Company: Join nxzen, a leader in location intelligence and geospatial solutions.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous improvement and learning.
- Why this job: Make a real impact in critical infrastructure sectors with innovative geospatial technology.
- Qualifications: Experience in Python, SQL, and geospatial data processing is essential.
The predicted salary is between 40000 - 50000 ÂŁ per year.
Joining nxzen’s rapidly growing geospatial capability means becoming part of a team redefining how organisations use location intelligence across critical infrastructure sectors. As a Geospatial Data Engineer, you will play a key role in building reliable spatial data pipelines, automated workflows, APIs, and transformation processes that underpin our next generation of geospatial solutions. Reporting to the UK GIS Lead, you will support day‑to‑day delivery across data‑engineering workstreams, contribute to solution implementation, and help ensure the smooth operation of our geospatial data workflows. This role combines hands‑on engineering with opportunities to influence technical approaches, helping to improve the reliability, automation, and performance of our pipelines.
You will build ETL processes, transform and validate geospatial datasets, support API and service implementations, contribute to CI/CD workflows, and enhance observability across our geospatial data operations.
The role reports to the UK GIS Lead and will be responsible for:
- Developing and maintaining end‑to‑end spatial ETL pipelines in collaboration with consultants, data engineers, architects, and platform teams.
- Implementing geospatial data services and APIs that reliably expose processed datasets for analytical, operational, and mapping use cases.
- Optimising query performance, indexing strategies, and storage patterns across spatial databases and cloud‑hosted datasets.
- Contributing to improvements in CI/CD processes that automate testing, validation, and deployment of geospatial data workflows.
- Enhancing observability through structured logging, data‑quality checks, lineage tracking, and pipeline health indicators.
Key Responsibilities
- Data Pipeline Delivery: Build and maintain geospatial ETL pipelines using Python, SQL, FME, ArcGIS tools, or similar technologies. Prepare, transform, validate, and load spatial datasets from multiple structured and unstructured sources. Implement workflow scheduling, automation, and repeatable processing patterns. Diagnose issues, contribute to root‑cause analysis, and implement stabilisation fixes. Support performance optimisation including indexing, partitioning, caching, and schema refinement.
- Data Services & API Implementation: Implement geospatial APIs and services to publish processed datasets. Configure secure access patterns, schema‑aware endpoints, and version‑controlled data outputs. Support integrations with upstream and downstream systems.
- Solution Implementation: Contribute to geospatial data‑engineering components of larger solutions. Configure data storage, ETL environments, and processing layers. Assist with deployment scripts, infrastructure configurations, and environment setup.
- Data & Analysis: Support data preparation, transformation, and migration activities using tools like ArcGIS Pro, FME, Python, or ModelBuilder. Develop repeatable processes for data quality, validation, and structured workflows.
- Observability, Quality & Reliability: Add structured logging, validation checks, and lineage tracking into pipelines. Contribute to dashboards monitoring pipeline health, reliability, and data‑quality metrics. Apply testing and engineering discipline to improve predictability and reduce defects.
- Documentation & Governance: Produce clear documentation describing pipeline behaviour, data flows, dependencies, and operational expectations. Maintain structured coding practices, version control discipline, and consistent naming and practices.
- Practice Support & Collaboration: Work closely with peers and senior engineers to adopt best‑practice engineering patterns. Participate in peer reviews and knowledge‑sharing sessions. Provide input into continuous improvement of internal engineering methods and processes.
Essential Skills & Experience:
- Competent with Python, SQL, PostgreSQL, and PostGIS to develop, optimise, and operate reliable geospatial data workflows.
- Strong hands‑on experience with geospatial data processing, ETL workflows, and spatial transformations.
- Practical expertise with geospatial data formats (e.g., Shapefile, GPKG, GeoJSON, FGDB, GeoTIFF, GeoParquet etc.), coordinate systems, and spatial data standards.
- Experience with cloud platforms (Azure, AWS, or GCP) for data engineering workloads.
- Experience using ESRI tools, FME, GDAL/OGR or equivalent technologies.
- Understanding of spatial indexing, query optimisation, schema design, and data‑model evolution.
- Experience building repeatable workflows for data validation, quality checks, logging, and structured processing.
- Ability to communicate technical details clearly and collaborate effectively across multidisciplinary teams.
- Strong problem‑solving skills and a methodical approach to debugging and optimisation.
Desirable (but not essential) Skills & Experience:
- Experience in utilities, infrastructure, or other asset‑intensive sectors.
- Experience with ArcGIS Utility Network is highly desirable.
- Familiarity with CI/CD pipelines, DevOps practices, and infrastructure‑as‑code.
- Exposure to APIs, microservices, or event‑driven data architectures.
- Knowledge of ArcGIS Enterprise, ArcGIS Online, or other geospatial platforms.
- Scripting or workflow automation experience beyond core data engineering.
- Exposure to open‑source geospatial tools and libraries.
Geospatial Engineer in London employer: nxzen Global
Contact Detail:
nxzen Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Geospatial Engineer in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the geospatial field. Attend meetups, webinars, or even local events. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving ETL pipelines or geospatial data processing. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach for each application. Research the company, understand their projects, and mention how your skills can specifically help them achieve their goals. This shows genuine interest and effort.
✨Tip Number 4
Apply through our website! We love seeing applications that come directly from our platform. It helps us keep track of candidates and gives you a better chance of standing out. Plus, it’s super easy to do!
We think you need these skills to ace Geospatial Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Geospatial Engineer role. Highlight your experience with Python, SQL, and any geospatial tools you've used. We want to see how your skills match what we're looking for!
Showcase Your Projects: Include specific projects where you've built ETL pipelines or worked with geospatial data. This gives us a clear picture of your hands-on experience and problem-solving skills in action.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at nxzen Global
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, SQL, and PostGIS. Brush up on your ETL processes and be ready to discuss how you've implemented them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of challenges you've faced in geospatial data processing and how you resolved them. This role requires strong problem-solving skills, so demonstrating your methodical approach will impress the interviewers.
✨Understand the Company’s Vision
Research nxzen’s geospatial capabilities and their impact on critical infrastructure sectors. Being able to articulate how your skills align with their mission will show that you’re genuinely interested in the role and the company.
✨Prepare for Technical Questions
Expect technical questions related to spatial data workflows, API implementations, and performance optimisation. Practise explaining complex concepts clearly, as communication is key in multidisciplinary teams.